What is the BlueInvest Project Pipeline?
See our showcase of innovative projects and entrepreneurs who are shaping the Blue Economy.
Projects consist of innovative, scalable and sustainable business ventures from traditional and emerging sectors of the maritime economy.
The graphs below provide an overview of the country distribution, sector, and TRL of projects currently listed in the BlueInvest Project Pipeline.
Selection Criteria:
Your company is EU-based or from an eligible sea basin region (Georgia, Turkey, Ukraine, Moldova, Montenegro, Albania, Bosnia-Herzegovina, North Macedonia, Serbia, Turkey, Israel, Egypt, Tunisia, Algeria, Morocco, Lebanon, Syria, Libya, Mauritania, United Kingdom, or Norway)
Your project TRL is 4 or higher
Your project sector is in one of the 10 blue economy sectors identified for this initiative
Your solution has a clear sustainable impact on the blue economy
Your project is looking for investment
Project Country
Country in which the project is being developed and / or implemented.
Project Sector
Main blue economy sector the project relates to.
Project TRL
Current technology readiness level of the project.
Toqua
In the coming years, shipping faces the challenge of making its fleet more carbon-efficient every year to comply with emission reduction regulations. The easiest option to reach these short-term decarbonization targets are operational optimizations (weather routing, maintenance optimization, speed optimization, etc.) and retrofits (air lubrication, flettner rotor, propeller attachments, premium paints, etc.).
The problem is that you can’t manage what you can’t measure.
Today's traditional ship performance modeling techniques (sea trial curves, ISO19030, Noon Report averages, ISO15016, ...) have average errors of around 10%-20%, making it impossible to measure and validate these single digit percentage savings. Factors like waves, wind, currents, loading conditions, fouling, etc. add noise to the modeling approach and make it impossible to accurately model a ship’s performance and validate fuel savings (as shown in the 2 figures below).
Finally, due to rising fuel prices, it’s not only compliance with emission regulations that’s a motivating factor, but also cost savings that form a main driver for better technology in this area.
- API first - we spend 100% of our resources to make the models better
- Focus on sensor data - we unlock the value of this data by providing high accuracy score for the models
- Physics informed ML - hybrid approach as it is data driven but still takes into account the physics